In the prior art, as a method for detecting a boundary line (white line) on a plane (road plane) from time series images input from a camera loaded on a vehicle, various kinds of image processing techniques are known.
As one method of a preprocessing method, the gradient of intensity values between the white line and the road plane is aimed. In this method, the edge intensity is binalized on a camera image (input image), and a white line candidate is restricted. For example, as for the binalized camera image, a plurality of templates of white lines are prepared, and the white line is detected by template matching. Alternatively, a plurality of white line candidates are prepared, and an assumed candidate for a white line on which most edge points exist is detected as the white line. In these methods, a threshold for binalization processing of edge intensity is set for preprocessing. However, it is difficult to set the threshold to the most suitable value irrespective of weather condition or illumination condition.
In the case of detecting a white line in front of the vehicle on the road, in general, the camera is set so that the optical axis of the camera is parallel to and lower than advancing direction of the vehicle. If the camera is set at this position, as shown in FIG. 1, a white line of the left side on the road plane is input as a curved line extended from the center neighborhood to the left lower side on the camera image. On the other hand, a white line of the right side on the road plane is input as a curved line extended from the center neighborhood to the right lower side on the camera image. In this case, as shown in FIGS. 2A, 2B, and 2C, the slope of the white line changes on the camera image in accordance with the vehicle's location on the road plane.
In the white line detection using a prior art template matching, a search area is set on the camera image in order to reduce the amount of calculations. However, it is necessary that the search area is adaptively changed because the slope of the white line on the camera image is changed by the vehicle's location. Accordingly, processing to set the search area is often complicated in the prior art.
In the same way, in the method for detecting the white line candidate in which most edge points exist as the white line, it is often necessary to set a direction of the white line candidate. However, processing to set the direction of the white line candidate is often complicated because the slope of the white line on the camera image is changed by the vehicle's location.
Furthermore, in the white line detection using the template matching, a width of the white line closer to the camera is different from that of a remote part on the camera image. In addition to this, in order to cope with a curved white line, many templates must be prepared. Additionally, the processing is complicated by selection of the suitable templates.
As mentioned-above, in the prior art, the following three problems exist.
(1) In a method for detecting a boundary line using a threshold processing such as binalization for edge detection, it is difficult to suitably set the threshold under a condition of bad weather, such as rainy weather. Accordingly, robust detection of the boundary line is difficult.
(2) In a method for detecting a boundary line on a time series images input from the camera, the slope of the white line on the camera image is changed by the vehicle's location on the road plane. Accordingly, processing to set the search area or the search direction is complicated, and the calculation load greatly increases.
(3) In a method for detecting a white line using template matching, the line width and the slope of the boundary line near the camera is different from that of a remote part on the camera image. Accordingly, a large number of templates must be prepared. Furthermore, decision processing to change the template is necessary. As a result, the calculation load greatly increases.